用大模型模拟多视角辩论，提升复杂问题解决能力
Thinking with Many Minds: Using Large Language Models for Multi-Perspective Problem-Solving
- 让多个虚拟角色扮演不同立场，协同讨论问题
- 可并行处理多视角且不丢失观点差异，效果优于单一思维
- 适合战略决策、政策制定等需要多角度思考的场景
复杂问题求解需要认知灵活性——即在保持各视角独立性的同时，同时考虑多种观点。这种灵活性实现了个体内部的“群体智慧”，使人能“以多心智思考”。尽管心理模拟可实现想象中的讨论，但认知资源有限，效率受限。本文提出合成式思辨（synthetic deliberation），一种基于大语言模型的方法，通过让代表不同立场的代理进行模拟对话来解决此问题。采用定制的GPT模型，实证表明该方法可实现多视角的并发处理而不出现认知退化，支持视角的并行探索，并能精确控制观点融合。通过将思辨过程外化并分配认知任务于并行搜索与整合，该方法突破了心理模拟的局限。该方法在战略规划、政策制定和冲突调解中具有应用潜力。
原文摘要 · Abstract (English)
Complex problem-solving requires cognitive flexibility--the capacity to entertain multiple perspectives while preserving their distinctiveness. This flexibility replicates the "wisdom of crowds" within a single individual, allowing them to "think with many minds." While mental simulation enables imagined deliberation, cognitive constraints limit its effectiveness. We propose synthetic deliberation, a Large Language Model (LLM)-based method that simulates discourse between agents embodying diverse perspectives, as a solution. Using a custom GPT-based model, we showcase its benefits: concurrent processing of multiple viewpoints without cognitive degradation, parallel exploration of perspectives, and precise control over viewpoint synthesis. By externalizing the deliberative process and distributing cognitive labor between parallel search and integration, synthetic deliberation transcends mental simulation's limitations. This approach shows promise for strategic planning, policymaking, and conflict resolution.
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